
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "54"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 132 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 127 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.083268	-0.638909	-0.584250	-1.099520	0.027378	-1.049313	-1.367916	-1.162920	0.7696	0.7636	0.2368	14.752073	12.130395
2459593	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.333095	-0.725412	0.310379	-1.130935	-0.134716	-1.612583	-0.497479	-1.184953	0.6916	0.6789	0.2241	6.671007	6.038527
2459592	RF_maintenance	0.00%	100.00%	100.00%	0.00%	-	-	0.497292	-0.532793	-0.126629	-0.732934	-0.280935	-0.400851	-1.942491	0.027036	0.0553	0.0473	0.0013	nan	nan
2459591	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	358.493172	358.420129	inf	inf	90.903697	97.981741	1112.786850	1198.907212	nan	nan	nan	0.000000	0.000000
2459590	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	362.396275	362.434010	inf	inf	32.011166	36.284341	212.266864	220.975304	nan	nan	nan	0.000000	0.000000
2459589	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.007197	-0.824147	0.335621	-1.130810	-0.480257	-1.006080	-0.671720	-1.184248	0.6721	0.6561	0.2427	7.259142	6.473605
2459588	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.702558	-0.545707	0.055523	-1.207241	0.896677	-1.247712	0.282407	-0.771242	0.7437	0.7233	0.2034	12.496200	9.777752
2459587	RF_maintenance	100.00%	-	-	-	100.00%	0.00%	0.902438	-0.518099	3.608550	3.139501	-23.333991	-23.858603	-4.675000	-4.393628	nan	nan	nan	72.777754	51.307405
2459586	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.750118	-0.789526	-0.019034	-0.874487	0.519154	-1.219772	0.784950	-0.953468	0.6021	0.6098	0.3113	4.399654	4.319322
2459585	RF_maintenance	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.403343	-1.226839	1.041781	-0.161178	0.260390	-0.421404	-0.673812	1.928607	0.0466	0.0554	0.0041	0.000000	0.000000
2459584	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.943340	3.779565	1.432489	0.603401	5.224406	4.208218	39.216901	36.173157	0.5582	0.5687	0.3205	3.352212	0.000000
2459583	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.353457	-0.448289	1.191895	-0.734838	1.267814	-0.959011	0.869417	-0.490360	0.5942	0.6061	0.3357	4.381988	4.272975
2459582	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	296.822411	296.682450	inf	inf	11458.727415	11415.534915	6068.092714	6059.196669	nan	nan	nan	0.000000	0.000000
2459581	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.838713	-0.648474	1.290089	-0.906363	1.873483	-0.981636	0.656609	-0.702362	0.6073	0.6128	0.3324	5.905324	4.988176
2459580	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.358046	-0.593439	0.801056	-0.765998	1.226263	-0.895781	-0.332467	-0.941946	0.6001	0.6125	0.3331	5.563101	4.988400
2459579	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.799988	-0.112046	1.187913	-0.353850	1.760351	-1.009139	0.225644	-0.569888	0.5882	0.6060	0.3358	5.636368	5.039970
2459578	RF_maintenance	0.00%	100.00%	100.00%	0.00%	-	-	-1.014520	0.104337	-0.401995	-0.619592	0.596669	-0.299047	0.186546	0.193339	0.0569	0.0608	0.0041	nan	nan
2459577	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.757274	-0.346080	0.861575	-0.684185	1.675449	-1.005450	1.489004	-0.510299	0.6051	0.6139	0.3315	6.551333	5.435774
2459576	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.400677	-0.397957	0.660520	-0.136904	1.961622	-0.809098	0.489256	-0.699959	0.6160	0.6233	0.3359	5.379612	5.032873
2459575	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.252575	-0.461330	0.675436	-0.416048	1.355888	-1.332932	0.956786	-0.515112	0.7036	0.6956	0.2998	2.210888	2.220921
2459574	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.544918	-0.323666	0.631915	-0.447720	1.565226	-0.731083	-0.019761	-0.667888	0.6205	0.6268	0.3336	4.860701	4.333811
2459573	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.459235	-0.338615	1.460321	-0.719122	2.078178	-1.470394	-0.245074	-0.508336	0.6128	0.6191	0.3406	0.000000	0.000000
2459572	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.214996	-0.614884	0.549391	-0.353936	0.988684	-0.980773	0.483606	-0.487674	0.6167	0.6224	0.3376	8.287329	6.581349
2459571	RF_maintenance	0.00%	100.00%	100.00%	0.00%	-	-	-0.689667	0.093531	-0.702379	-0.690647	-0.285005	-1.092130	0.185687	0.313549	0.0613	0.0806	0.0073	nan	nan
2459570	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.257945	-0.039310	1.041701	0.349576	1.148919	0.827606	0.640832	0.666972	0.6249	0.6404	0.3128	34.318663	23.419963
2459569	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.043918	-0.381698	0.802795	-0.514322	0.420652	-0.728992	0.731749	-0.652881	0.7331	0.7303	0.1988	6.371660	5.386258
2459566	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.013641	-0.419396	1.113971	-0.558581	2.031001	-0.906882	1.076830	-0.129546	0.5990	0.6125	0.3518	0.000000	0.000000
2459565	RF_maintenance	100.00%	-	-	-	100.00%	0.00%	137320.428719	127536.465420	4248046.013608	4422252.038733	nan	nan	-5.052525	-5.052525	nan	nan	nan	0.000000	0.000000
2459564	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.676654	1.460696	0.723844	1.204498	-1.167177	-0.917011	-1.015351	-1.374029	0.6187	0.6191	0.3340	3.782625	3.075502
2459563	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.824594	5.162333	19.178194	20.626067	8.520492	12.406403	-1.443395	-1.855541	0.6103	0.6105	0.3427	0.000000	0.000000
2459562	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.095535	0.594826	-0.127508	0.134062	-1.745842	-1.273460	-1.215350	-1.332926	0.6165	0.6207	0.3384	3.935980	3.186897
2459561	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.223583	1.177667	0.345929	0.728023	-1.537672	-0.817991	-1.997652	-2.233535	0.6246	0.6305	0.3378	4.717273	4.483624
2459560	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459559	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.391280	1.486160	0.543906	0.989463	-0.954724	0.044219	-1.378445	-1.565489	0.6203	0.6258	0.3315	3.418567	2.911752
2459558	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.952303	5.426899	10.295599	10.743970	8.936222	12.344524	-0.691863	-1.833296	0.6103	0.6127	0.3382	4.665740	3.996555
2459557	RF_maintenance	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0917	0.1142	0.0198	nan	nan
2459556	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.290687	5.736768	10.770729	11.107343	8.570160	11.089147	-2.562058	-3.322141	0.6161	0.6211	0.3266	5.434102	4.488090
2459553	RF_maintenance	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6106	0.6160	0.3329	nan	nan
2459552	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.220708	6.353673	14.105331	15.489664	13.503749	17.410567	0.409494	-0.042145	0.6304	0.6266	0.3382	0.000000	0.000000
2459551	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.280478	1.110458	0.212480	0.680016	-1.904918	-0.597505	-1.608627	-1.986970	0.6213	0.6209	0.3300	4.058465	3.303844
2459550	RF_maintenance	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0643	0.0820	0.0152	nan	nan
2459549	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.096745	5.223927	10.279577	11.285202	9.039770	12.199246	0.224318	-2.072083	0.6163	0.6222	0.3365	3.821811	4.093603
2459542	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.961355	5.334099	20.919487	22.640201	0.203450	0.801446	-1.276132	-1.911582	0.6762	0.6813	0.3085	4.243783	3.733794
2459541	RF_maintenance	100.00%	1.34%	1.34%	0.00%	100.00%	0.00%	4.654236	4.411588	13.259992	13.278178	8.440985	10.264064	3.986205	3.508574	0.6783	0.6769	0.2836	2.203915	2.073446
2459540	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.604053	2.086851	1.715913	1.940219	-0.206478	-0.052563	-0.577284	-0.879853	0.6851	0.6811	0.3037	3.599159	3.151542
2459536	RF_maintenance	100.00%	37.06%	37.06%	0.00%	100.00%	0.00%	6.690081	8.198944	17.558702	18.678950	15.891048	17.718722	-0.401757	-3.747322	0.4074	0.4111	0.2119	1.640878	1.592624
2459535	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459534	RF_maintenance	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.166774	1.135744	0.204638	0.851374	-1.030286	-0.912829	-0.482584	-1.369006	0.0607	0.0739	0.0096	1.178990	1.177000
2459533	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	4.141580	5.669208	10.628535	11.559991	7.846994	11.248267	-2.125523	-2.259320	0.0557	0.0699	0.0081	1.149038	1.144517
2459532	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	5.187112	6.627324	14.111204	14.764750	10.794889	13.748016	-1.331580	-2.234871	0.0544	0.0664	0.0075	0.845599	0.839594
2459530	RF_maintenance	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.048111	1.038353	0.244298	0.634556	-1.520556	-0.525420	-0.499853	-1.329831	0.0603	0.0738	0.0104	1.177135	1.177000
2459527	RF_maintenance	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.157274	6.887383	11.158916	12.385071	23.023774	21.155567	-1.159651	-1.210208	0.9095	0.9353	0.2921	0.000000	0.000000
2459523	RF_maintenance	100.00%	-	-	-	100.00%	0.00%	4.670143	6.618210	15.342302	17.308589	9.280402	16.390278	-4.093783	-4.862819	nan	nan	nan	10.499490	11.640062
2459522	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.101931	5.570704	14.237040	14.760784	5.002619	14.554899	-1.467644	-2.573232	0.6955	0.6782	0.3082	2.851183	2.927918
2459513	RF_maintenance	100.00%	100.00%	100.00%	0.00%	-	-	4.126694	4.529433	5.242662	6.768838	6.607780	3.724780	8.188367	5.098558	0.0617	0.0740	0.0177	nan	nan
2459508	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	5.297158	7.300623	8.854197	9.413586	9.649171	8.478366	7.936496	10.516775	0.0496	0.0688	0.0042	0.000000	0.000000
2459505	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	288.712963	96.073889	166.232091	110.957650	146.608424	140.568220	1017.065191	692.322018	0.0068	0.0068	0.0070	1.130863	1.134746

Antenna Season Report Notebook¶

Antenna 54 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 54, Node 4:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

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Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶